The Importance of Iteration in Innovation Labs

Another AI proof-of-concept stalled in the sandbox, sound familiar? Over the past few years I’ve reviewed dozens of enterprise POCs that looked flawless in the demo, yet never made it past the “innovation lab.” Four patterns keep popping up: 1. The hype cycle wins the brief. Vision decks promise quantum leaps, but the first sprint still needs clean data, stable infra, and a clear business metric. When the gap between the slide and reality shows up, confidence—and budgets—shrink fast. 2. Teams treat AI like a light switch, not a feedback loop. Great models evolve through tight, iterative loops: ship → measure → refine. When leadership expects a turnkey solution, product teams hide the rough edges instead of surfacing them for incremental improvement, and the learning curve stalls. 3. “Works on my laptop” syndrome. That dazzling demo ran on hand-picked inputs, perfect network latency, and curated prompts. Push the same pipeline into production traffic, multiple locales, edge-case data, compliance constraints, and brittle context engineering falls apart. Scale reveals what slideware conceals. 4. No robust evaluation harness. If you can’t measure the model the same way you measure the business, you’re flying blind. Reliable offline test suites, synthetic edge-case generators, and live A/B safeguards are non-negotiable. Skip them and every regression becomes a guessing game. How I de-risk new AI work: - Start with the smallest slice that still proves value, but instrument it end-to-end. - Write the eval plan before the first line of code. Benchmarks drive architecture decisions. - Budget for “unknown unknowns.” Iteration time is a line item, not a contingency. - Run shadow deployments early. Let real-world noise teach the model while stakes are low. - Refine the success narrative as data arrives. Outcomes > algorithms. - Shifting from demo theatrics to disciplined engineering is hard, but that’s where the compounding returns live. The next time a stakeholder asks, “Can we see a quick POC?” try reframing the ask: “Sure, let’s design a mini production line instead of a one-off magic trick.” That single mindset change often saves months, and gives the project a real shot at production.

Major barrier to accelerating innovation for hardware is dominant waterfall thinking. The V&V development process epitomizes this mindset, which we frequently see with our large clients. Machina Labs’ tech offers numerous advantages depending on the application, such as shortened lead times, reduced costs, or enabling processing of entirely new material. However, the common, but outdated, approach is asking whether our process can directly replace a sub component or even a part in existing one. We found out we need to work with our customers in a different way: starting with the business case and potential end-to-end benefits, and aiming for at least a tenfold improvement. This approach involves challenging all existing requirements and using Robocraftsman to develop a product from start to finish. Our tech’s super power is order of magnitude faster iteration compared to traditional techniques, moving quickly from feedstock to the final system to assess the benefits comprehensively for the final system as opposed to its subsystems. It also means we rapidly add capabilities to Robocraftsman along the way. For instance, if you’re considering building the lunar lander fuel tank, it is common to start replacing and meeting requirements for individual sheet metal panels one at a time with Roboforming, but we propose constructing the entire tank end-to-end and rapidly iterate on that cycle, only evaluating the global performance of the final system. The faster that cycle is the better the improvements become at the end. Each iteration focused on removing one major risk. This method allows us to see how the new process can enhance every stage of product development, from raw materials to the completed system using Robocraftsman. This shift away from linear, step-by-step thinking to embracing rapid, end-to-end iteration may be challenging, but it’s crucial for effectively integrating the Robocraftsman platform and delivering at least tenfold value to our customers. I came across Charles Kuehmann insights on this topic at the NAE. His work at SpaceX and Tesla is pioneering in this type of approach. https://lnkd.in/gRpUTAuT

Innovation doesn’t always mean reinvention. Arguably, iteration is how many game-changing technologies are built. Take Apple and BlackBerry. BlackBerry had proven the smartphone category worked, but there was massive room for improvement. Apple didn't invent the smartphone; they transformed it. Back in 2020, we saw the same opportunity with intravascular lithotripsy (IVL). In just two years, IVL went from zero utilization to being used in over 10% of PCI procedures in the U.S.— and its adoption only continues to grow. In fact, most reports suggest the therapy will be used in over 20% of coronary interventions by the end of this decade. Based on some nascent research suggesting laser-based systems could generate the same therapeutic shock waves as electric platforms, we saw a huge opportunity to iterate on proven first-generation technology. The potential: A laser system that could offer a more efficient, predictable, and controllable technology in comparison to electric IVL platforms that generate shock waves from spark discharges. The challenge: Coronary vessels have a very unique set of requirements. - Small, tortuous anatomy; and - Patients with compromised cardiac output requiring faster therapy delivery; and - The risk that comes with electromagnetic interference operating close to the heart. These hurdles demanded solutions that existing technology couldn't provide. A laser-based IVL platform hadn’t been developed before, but we believed it could be. By early 2023, after a few years of extensive R&D work, we hit an inflection point. We finally thought we could pull it off. Fast forward to May of this year and we’re now in the throes of a coronary feasibility study. The lesson: You don't always need to reinvent the wheel. Instead, you can build a better wheel (like Apple did). Iteration on proven technology can be just as transformative as starting from scratch.